Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations19096
Missing cells138
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.0 MiB
Average record size in memory770.0 B

Variable types

Numeric7
Text4
Categorical4
DateTime3
Unsupported2

Alerts

discount_amount is highly overall correlated with order_id and 1 other fieldsHigh correlation
order_id is highly overall correlated with discount_amount and 1 other fieldsHigh correlation
ticket_id is highly overall correlated with discount_amount and 1 other fieldsHigh correlation
passenger_age is highly skewed (γ1 = 138.1694074) Skewed
insurance_fee is highly skewed (γ1 = 20.20000787) Skewed
ticket_id has unique values Unique
departure_time is an unsupported type, check if it needs cleaning or further analysis Unsupported
arrival_time is an unsupported type, check if it needs cleaning or further analysis Unsupported
baggage has 17290 (90.5%) zeros Zeros
insurance_fee has 19053 (99.8%) zeros Zeros
discount_amount has 6561 (34.4%) zeros Zeros

Reproduction

Analysis started2025-04-27 08:56:55.524048
Analysis finished2025-04-27 08:57:02.416960
Duration6.89 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

order_id
Real number (ℝ)

High correlation 

Distinct12422
Distinct (%)65.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26922.722
Minimum20142
Maximum33943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size149.3 KiB
2025-04-27T15:57:02.710403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20142
5-th percentile20786
Q123431.75
median26826
Q330361.25
95-th percentile33272.5
Maximum33943
Range13801
Interquartile range (IQR)6929.5

Descriptive statistics

Standard deviation3999.9958
Coefficient of variation (CV)0.14857323
Kurtosis-1.1952913
Mean26922.722
Median Absolute Deviation (MAD)3460.5
Skewness0.049492992
Sum5.1411631 × 108
Variance15999966
MonotonicityNot monotonic
2025-04-27T15:57:02.868882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28302 16
 
0.1%
26506 16
 
0.1%
26503 15
 
0.1%
27048 15
 
0.1%
28301 15
 
0.1%
24734 13
 
0.1%
30579 13
 
0.1%
21642 12
 
0.1%
24219 12
 
0.1%
21026 12
 
0.1%
Other values (12412) 18957
99.3%
ValueCountFrequency (%)
20142 2
< 0.1%
20143 2
< 0.1%
20144 2
< 0.1%
20145 2
< 0.1%
20146 1
 
< 0.1%
20147 2
< 0.1%
20148 1
 
< 0.1%
20149 2
< 0.1%
20150 2
< 0.1%
20151 3
< 0.1%
ValueCountFrequency (%)
33943 1
 
< 0.1%
33942 1
 
< 0.1%
33941 1
 
< 0.1%
33940 2
 
< 0.1%
33939 1
 
< 0.1%
33938 1
 
< 0.1%
33935 3
 
< 0.1%
33934 2
 
< 0.1%
33929 8
< 0.1%
33928 1
 
< 0.1%

ticket_id
Real number (ℝ)

High correlation  Unique 

Distinct19096
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41841.328
Minimum31164
Maximum52769
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size149.3 KiB
2025-04-27T15:57:03.021494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum31164
5-th percentile32228.75
Q136484.75
median41807.5
Q347093.25
95-th percentile51687.5
Maximum52769
Range21605
Interquartile range (IQR)10608.5

Descriptive statistics

Standard deviation6199.5059
Coefficient of variation (CV)0.14816704
Kurtosis-1.1743666
Mean41841.328
Median Absolute Deviation (MAD)5305.5
Skewness0.027185024
Sum7.99002 × 108
Variance38433873
MonotonicityNot monotonic
2025-04-27T15:57:03.154973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52669 1
 
< 0.1%
31268 1
 
< 0.1%
31242 1
 
< 0.1%
31337 1
 
< 0.1%
31360 1
 
< 0.1%
31355 1
 
< 0.1%
31348 1
 
< 0.1%
31307 1
 
< 0.1%
52602 1
 
< 0.1%
52677 1
 
< 0.1%
Other values (19086) 19086
99.9%
ValueCountFrequency (%)
31164 1
< 0.1%
31165 1
< 0.1%
31166 1
< 0.1%
31167 1
< 0.1%
31168 1
< 0.1%
31169 1
< 0.1%
31170 1
< 0.1%
31171 1
< 0.1%
31172 1
< 0.1%
31173 1
< 0.1%
ValueCountFrequency (%)
52769 1
< 0.1%
52767 1
< 0.1%
52766 1
< 0.1%
52764 1
< 0.1%
52763 1
< 0.1%
52761 1
< 0.1%
52760 1
< 0.1%
52754 1
< 0.1%
52753 1
< 0.1%
52752 1
< 0.1%
Distinct5282
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2025-04-27T15:57:03.382403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length44
Median length44
Mean length44
Min length44

Characters and Unicode

Total characters840224
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1975 ?
Unique (%)10.3%

Sample

1st rowXs1fLinPQun+Oy/mD0FlfXMyJD38YlXJfXNbz+qO+5U=
2nd rowm/auVlEoaGSe2MqR/Lq4CFXJnI8zauNyF/KiFmMgXHs=
3rd row6HvtV2vTqUfNBJ53C4TzvJnb0s74KgcRNuUVS+8jRvU=
4th rowWiK2P31WEd4mckRNVWz0C2aKKRgSrauAG8NdfTLnKjE=
5th row6HvtV2vTqUfNBJ53C4TzvJnb0s74KgcRNuUVS+8jRvU=
ValueCountFrequency (%)
7mhtyxxvgw06awi7uyjjojbxcnsaz7ivqkkpa3oc4zm 588
 
3.1%
rtw0btvvqbxqizvebr6hh/j9god8piy3mdszo+vozcq 341
 
1.8%
i0eiiyr+5um9iesxjj9fgunnnjnap0dqagpbsjpwp/a 125
 
0.7%
vi/lz6sujqxtp1msegaa/3egypfm0aavgs/tqf6xcwi 112
 
0.6%
tx4mtskll4v/ooar52nfe28fezyzbmhfjuobbepm1xs 92
 
0.5%
mjy6huul7qyrs4yyrtpg4se3c+t83gwxusqbxl0jngy 88
 
0.5%
xcghxo/oo7jui6xnb7jj+j1p0m5f9cseitx/3aqswbo 73
 
0.4%
soiaevbvr7zq5d3+iblzf+pvx6u9kcchbwmiue3vwyg 72
 
0.4%
f93nlcwewdxp8ti0zn3sqdbwxrhtlbixyukkcztsfvi 70
 
0.4%
ngq3kueexa/xfmxabympo/3edazikvbgaxiawfv60x4 52
 
0.3%
Other values (5272) 17483
91.6%
2025-04-27T15:57:03.657362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
= 19096
 
2.3%
A 14821
 
1.8%
M 14758
 
1.8%
I 14664
 
1.7%
o 14652
 
1.7%
0 14530
 
1.7%
g 13951
 
1.7%
X 13902
 
1.7%
7 13886
 
1.7%
c 13777
 
1.6%
Other values (55) 692187
82.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 840224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
= 19096
 
2.3%
A 14821
 
1.8%
M 14758
 
1.8%
I 14664
 
1.7%
o 14652
 
1.7%
0 14530
 
1.7%
g 13951
 
1.7%
X 13902
 
1.7%
7 13886
 
1.7%
c 13777
 
1.6%
Other values (55) 692187
82.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 840224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
= 19096
 
2.3%
A 14821
 
1.8%
M 14758
 
1.8%
I 14664
 
1.7%
o 14652
 
1.7%
0 14530
 
1.7%
g 13951
 
1.7%
X 13902
 
1.7%
7 13886
 
1.7%
c 13777
 
1.6%
Other values (55) 692187
82.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 840224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
= 19096
 
2.3%
A 14821
 
1.8%
M 14758
 
1.8%
I 14664
 
1.7%
o 14652
 
1.7%
0 14530
 
1.7%
g 13951
 
1.7%
X 13902
 
1.7%
7 13886
 
1.7%
c 13777
 
1.6%
Other values (55) 692187
82.4%

passenger_gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing13
Missing (%)0.1%
Memory size1008.1 KiB
Female
10027 
Male
9056 

Length

Max length6
Median length6
Mean length5.050883
Min length4

Characters and Unicode

Total characters96386
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 10027
52.5%
Male 9056
47.4%
(Missing) 13
 
0.1%

Length

2025-04-27T15:57:03.899012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-27T15:57:03.992551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female 10027
52.5%
male 9056
47.5%

Most occurring characters

ValueCountFrequency (%)
e 29110
30.2%
a 19083
19.8%
l 19083
19.8%
F 10027
 
10.4%
m 10027
 
10.4%
M 9056
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96386
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 29110
30.2%
a 19083
19.8%
l 19083
19.8%
F 10027
 
10.4%
m 10027
 
10.4%
M 9056
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96386
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 29110
30.2%
a 19083
19.8%
l 19083
19.8%
F 10027
 
10.4%
m 10027
 
10.4%
M 9056
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96386
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 29110
30.2%
a 19083
19.8%
l 19083
19.8%
F 10027
 
10.4%
m 10027
 
10.4%
M 9056
 
9.4%

passenger_age
Real number (ℝ)

Skewed 

Distinct92
Distinct (%)0.5%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean82.64093
Minimum-1
Maximum999999
Zeros25
Zeros (%)0.1%
Negative2
Negative (%)< 0.1%
Memory size149.3 KiB
2025-04-27T15:57:04.105401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile8
Q122
median28
Q336
95-th percentile59
Maximum999999
Range1000000
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7237.2387
Coefficient of variation (CV)87.574507
Kurtosis19090.857
Mean82.64093
Median Absolute Deviation (MAD)6
Skewness138.16941
Sum1577698
Variance52377624
MonotonicityNot monotonic
2025-04-27T15:57:04.246485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 950
 
5.0%
29 947
 
5.0%
23 946
 
5.0%
30 923
 
4.8%
24 917
 
4.8%
27 846
 
4.4%
22 838
 
4.4%
19 801
 
4.2%
20 795
 
4.2%
21 771
 
4.0%
Other values (82) 10357
54.2%
ValueCountFrequency (%)
-1 2
 
< 0.1%
0 25
 
0.1%
1 121
0.6%
2 222
1.2%
3 129
0.7%
4 123
0.6%
5 145
0.8%
6 116
0.6%
7 71
 
0.4%
8 115
0.6%
ValueCountFrequency (%)
999999 1
 
< 0.1%
115 1
 
< 0.1%
91 3
 
< 0.1%
90 3
 
< 0.1%
87 5
< 0.1%
86 1
 
< 0.1%
85 8
< 0.1%
84 5
< 0.1%
83 5
< 0.1%
82 9
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size988.2 KiB
IOS
12369 
Android
4700 
GYL
2027 

Length

Max length7
Median length3
Mean length3.9844994
Min length3

Characters and Unicode

Total characters76088
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIOS
2nd rowGYL
3rd rowIOS
4th rowAndroid
5th rowIOS

Common Values

ValueCountFrequency (%)
IOS 12369
64.8%
Android 4700
 
24.6%
GYL 2027
 
10.6%

Length

2025-04-27T15:57:04.375380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-27T15:57:04.468657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ios 12369
64.8%
android 4700
 
24.6%
gyl 2027
 
10.6%

Most occurring characters

ValueCountFrequency (%)
I 12369
16.3%
O 12369
16.3%
S 12369
16.3%
d 9400
12.4%
A 4700
 
6.2%
n 4700
 
6.2%
r 4700
 
6.2%
o 4700
 
6.2%
i 4700
 
6.2%
G 2027
 
2.7%
Other values (2) 4054
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 12369
16.3%
O 12369
16.3%
S 12369
16.3%
d 9400
12.4%
A 4700
 
6.2%
n 4700
 
6.2%
r 4700
 
6.2%
o 4700
 
6.2%
i 4700
 
6.2%
G 2027
 
2.7%
Other values (2) 4054
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 12369
16.3%
O 12369
16.3%
S 12369
16.3%
d 9400
12.4%
A 4700
 
6.2%
n 4700
 
6.2%
r 4700
 
6.2%
o 4700
 
6.2%
i 4700
 
6.2%
G 2027
 
2.7%
Other values (2) 4054
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 12369
16.3%
O 12369
16.3%
S 12369
16.3%
d 9400
12.4%
A 4700
 
6.2%
n 4700
 
6.2%
r 4700
 
6.2%
o 4700
 
6.2%
i 4700
 
6.2%
G 2027
 
2.7%
Other values (2) 4054
 
5.3%

ticket_status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size978.6 KiB
New
10401 
Paid
8536 
Cancel
 
159

Length

Max length6
Median length3
Mean length3.4719837
Min length3

Characters and Unicode

Total characters66301
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPaid
2nd rowPaid
3rd rowPaid
4th rowNew
5th rowPaid

Common Values

ValueCountFrequency (%)
New 10401
54.5%
Paid 8536
44.7%
Cancel 159
 
0.8%

Length

2025-04-27T15:57:04.570866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-27T15:57:04.650753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
new 10401
54.5%
paid 8536
44.7%
cancel 159
 
0.8%

Most occurring characters

ValueCountFrequency (%)
e 10560
15.9%
N 10401
15.7%
w 10401
15.7%
a 8695
13.1%
P 8536
12.9%
i 8536
12.9%
d 8536
12.9%
C 159
 
0.2%
n 159
 
0.2%
c 159
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66301
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10560
15.9%
N 10401
15.7%
w 10401
15.7%
a 8695
13.1%
P 8536
12.9%
i 8536
12.9%
d 8536
12.9%
C 159
 
0.2%
n 159
 
0.2%
c 159
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66301
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10560
15.9%
N 10401
15.7%
w 10401
15.7%
a 8695
13.1%
P 8536
12.9%
i 8536
12.9%
d 8536
12.9%
C 159
 
0.2%
n 159
 
0.2%
c 159
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66301
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10560
15.9%
N 10401
15.7%
w 10401
15.7%
a 8695
13.1%
P 8536
12.9%
i 8536
12.9%
d 8536
12.9%
C 159
 
0.2%
n 159
 
0.2%
c 159
 
0.2%
Distinct92
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size149.3 KiB
Minimum2023-03-01 00:00:00
Maximum2023-05-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-27T15:57:04.757193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:04.911139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct188
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size149.3 KiB
Minimum2023-03-01 00:00:00
Maximum2024-01-26 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-27T15:57:05.063898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:05.202585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

departure_time
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size895.3 KiB
Distinct188
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size149.3 KiB
Minimum2023-03-01 00:00:00
Maximum2024-01-26 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-27T15:57:05.341746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:05.480933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

arrival_time
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size895.3 KiB
Distinct119
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2025-04-27T15:57:05.597681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters171864
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowBMV - SGN
2nd rowBMV - SGN
3rd rowHUI - SGN
4th rowHUI - SGN
5th rowHUI - SGN
ValueCountFrequency (%)
19096
33.3%
sgn 14897
26.0%
han 5878
 
10.3%
dad 3259
 
5.7%
cxr 2924
 
5.1%
dli 1866
 
3.3%
bmv 1661
 
2.9%
pxu 1243
 
2.2%
hui 961
 
1.7%
vii 885
 
1.5%
Other values (13) 4618
 
8.1%
2025-04-27T15:57:05.795143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
38192
22.2%
N 20777
12.1%
- 19096
11.1%
S 15028
 
8.7%
G 14908
 
8.7%
H 9652
 
5.6%
A 9405
 
5.5%
D 9026
 
5.3%
I 5431
 
3.2%
C 4892
 
2.8%
Other values (12) 25457
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 171864
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
38192
22.2%
N 20777
12.1%
- 19096
11.1%
S 15028
 
8.7%
G 14908
 
8.7%
H 9652
 
5.6%
A 9405
 
5.5%
D 9026
 
5.3%
I 5431
 
3.2%
C 4892
 
2.8%
Other values (12) 25457
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 171864
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
38192
22.2%
N 20777
12.1%
- 19096
11.1%
S 15028
 
8.7%
G 14908
 
8.7%
H 9652
 
5.6%
A 9405
 
5.5%
D 9026
 
5.3%
I 5431
 
3.2%
C 4892
 
2.8%
Other values (12) 25457
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 171864
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
38192
22.2%
N 20777
12.1%
- 19096
11.1%
S 15028
 
8.7%
G 14908
 
8.7%
H 9652
 
5.6%
A 9405
 
5.5%
D 9026
 
5.3%
I 5431
 
3.2%
C 4892
 
2.8%
Other values (12) 25457
14.8%
Distinct115
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
2025-04-27T15:57:05.925211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length27
Mean length21.07855
Min length16

Characters and Unicode

Total characters402516
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowĐắk Lắk - Hồ Chí Minh
2nd rowĐắk Lắk - Hồ Chí Minh
3rd rowThừa Thiên Huế - Hồ Chí Minh
4th rowThừa Thiên Huế - Hồ Chí Minh
5th rowThừa Thiên Huế - Hồ Chí Minh
ValueCountFrequency (%)
19096
17.1%
chí 14897
13.4%
hồ 14897
13.4%
minh 14897
13.4%
5878
 
5.3%
nội 5878
 
5.3%
đà 3259
 
2.9%
nẵng 3259
 
2.9%
khánh 2924
 
2.6%
hòa 2924
 
2.6%
Other values (33) 23560
21.1%
2025-04-27T15:57:06.202094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
92373
22.9%
h 42438
10.5%
n 31459
 
7.8%
i 26612
 
6.6%
H 25599
 
6.4%
- 19227
 
4.8%
16763
 
4.2%
C 15165
 
3.8%
M 14903
 
3.7%
í 14897
 
3.7%
Other values (37) 103080
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 402516
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
92373
22.9%
h 42438
10.5%
n 31459
 
7.8%
i 26612
 
6.6%
H 25599
 
6.4%
- 19227
 
4.8%
16763
 
4.2%
C 15165
 
3.8%
M 14903
 
3.7%
í 14897
 
3.7%
Other values (37) 103080
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 402516
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
92373
22.9%
h 42438
10.5%
n 31459
 
7.8%
i 26612
 
6.6%
H 25599
 
6.4%
- 19227
 
4.8%
16763
 
4.2%
C 15165
 
3.8%
M 14903
 
3.7%
í 14897
 
3.7%
Other values (37) 103080
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 402516
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
92373
22.9%
h 42438
10.5%
n 31459
 
7.8%
i 26612
 
6.6%
H 25599
 
6.4%
- 19227
 
4.8%
16763
 
4.2%
C 15165
 
3.8%
M 14903
 
3.7%
í 14897
 
3.7%
Other values (37) 103080
25.6%

airline
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Ry/OjEDP12vfMWbnat5Mudc3PE4qIK/wwfCkBE5zGlM=
12519 
V2Ej16fVems7wWmAIxP6MCYYH+i4YSHlvuJgKvD2q8A=
3489 
z7BiGsH86CZWRj0PbuuLk/5jh7XcwKSaa21M2royGnA=
1930 
73SByZiBulQv8bk/PGRtuNIpW3R+36DZ9nM31hDpDMQ=
 
1158

Length

Max length44
Median length44
Mean length44
Min length44

Characters and Unicode

Total characters840224
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRy/OjEDP12vfMWbnat5Mudc3PE4qIK/wwfCkBE5zGlM=
2nd rowRy/OjEDP12vfMWbnat5Mudc3PE4qIK/wwfCkBE5zGlM=
3rd rowRy/OjEDP12vfMWbnat5Mudc3PE4qIK/wwfCkBE5zGlM=
4th rowRy/OjEDP12vfMWbnat5Mudc3PE4qIK/wwfCkBE5zGlM=
5th rowRy/OjEDP12vfMWbnat5Mudc3PE4qIK/wwfCkBE5zGlM=

Common Values

ValueCountFrequency (%)
Ry/OjEDP12vfMWbnat5Mudc3PE4qIK/wwfCkBE5zGlM= 12519
65.6%
V2Ej16fVems7wWmAIxP6MCYYH+i4YSHlvuJgKvD2q8A= 3489
 
18.3%
z7BiGsH86CZWRj0PbuuLk/5jh7XcwKSaa21M2royGnA= 1930
 
10.1%
73SByZiBulQv8bk/PGRtuNIpW3R+36DZ9nM31hDpDMQ= 1158
 
6.1%

Length

2025-04-27T15:57:06.326156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-27T15:57:06.425253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ry/ojedp12vfmwbnat5mudc3pe4qik/wwfckbe5zglm 12519
65.6%
v2ej16fvems7wwmaixp6mcyyh+i4yshlvujgkvd2q8a 3489
 
18.3%
z7bigsh86czwrj0pbuulk/5jh7xcwksaa21m2roygna 1930
 
10.1%
73sbyzibulqv8bk/pgrtunipw3r+36dz9nm31hdpdmq 1158
 
6.1%

Most occurring characters

ValueCountFrequency (%)
M 45292
 
5.4%
E 41046
 
4.9%
P 31615
 
3.8%
w 30457
 
3.6%
f 28527
 
3.4%
/ 28126
 
3.3%
5 26968
 
3.2%
2 23357
 
2.8%
u 22184
 
2.6%
v 20655
 
2.5%
Other values (52) 541997
64.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 840224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 45292
 
5.4%
E 41046
 
4.9%
P 31615
 
3.8%
w 30457
 
3.6%
f 28527
 
3.4%
/ 28126
 
3.3%
5 26968
 
3.2%
2 23357
 
2.8%
u 22184
 
2.6%
v 20655
 
2.5%
Other values (52) 541997
64.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 840224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 45292
 
5.4%
E 41046
 
4.9%
P 31615
 
3.8%
w 30457
 
3.6%
f 28527
 
3.4%
/ 28126
 
3.3%
5 26968
 
3.2%
2 23357
 
2.8%
u 22184
 
2.6%
v 20655
 
2.5%
Other values (52) 541997
64.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 840224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 45292
 
5.4%
E 41046
 
4.9%
P 31615
 
3.8%
w 30457
 
3.6%
f 28527
 
3.4%
/ 28126
 
3.3%
5 26968
 
3.2%
2 23357
 
2.8%
u 22184
 
2.6%
v 20655
 
2.5%
Other values (52) 541997
64.5%
Distinct69
Distinct (%)0.4%
Missing120
Missing (%)0.6%
Memory size1012.5 KiB
2025-04-27T15:57:06.627748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length6
Mean length5.4256429
Min length1

Characters and Unicode

Total characters102957
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowI1_ECO
2nd rowZ1_ECO
3rd rowJ1_ECO
4th rowJ1_ECO
5th rowJ1_ECO
ValueCountFrequency (%)
e1_eco 3163
16.7%
a1_eco 2652
14.0%
z1_eco 2608
13.7%
economysaver 1622
 
8.5%
w1_eco 1269
 
6.7%
u1_eco 826
 
4.4%
e 784
 
4.1%
j1_eco 709
 
3.7%
a 632
 
3.3%
p 595
 
3.1%
Other values (59) 4116
21.7%
2025-04-27T15:57:06.870891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 18350
17.8%
C 12629
12.3%
O 12528
12.2%
_ 12519
12.2%
1 12515
12.2%
o 3832
 
3.7%
A 3303
 
3.2%
Z 2614
 
2.5%
m 2154
 
2.1%
n 1930
 
1.9%
Other values (34) 20583
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 102957
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 18350
17.8%
C 12629
12.3%
O 12528
12.2%
_ 12519
12.2%
1 12515
12.2%
o 3832
 
3.7%
A 3303
 
3.2%
Z 2614
 
2.5%
m 2154
 
2.1%
n 1930
 
1.9%
Other values (34) 20583
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 102957
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 18350
17.8%
C 12629
12.3%
O 12528
12.2%
_ 12519
12.2%
1 12515
12.2%
o 3832
 
3.7%
A 3303
 
3.2%
Z 2614
 
2.5%
m 2154
 
2.1%
n 1930
 
1.9%
Other values (34) 20583
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 102957
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 18350
17.8%
C 12629
12.3%
O 12528
12.2%
_ 12519
12.2%
1 12515
12.2%
o 3832
 
3.7%
A 3303
 
3.2%
Z 2614
 
2.5%
m 2154
 
2.1%
n 1930
 
1.9%
Other values (34) 20583
20.0%

ticket_price
Real number (ℝ)

Distinct2357
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1044120.7
Minimum3000
Maximum9010800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size149.3 KiB
2025-04-27T15:57:06.972551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3000
5-th percentile553600
Q1737520
median956800
Q31255100
95-th percentile1825200
Maximum9010800
Range9007800
Interquartile range (IQR)517580

Descriptive statistics

Standard deviation468967.61
Coefficient of variation (CV)0.44915075
Kurtosis29.040846
Mean1044120.7
Median Absolute Deviation (MAD)236370
Skewness2.9618958
Sum1.993853 × 1010
Variance2.1993062 × 1011
MonotonicityNot monotonic
2025-04-27T15:57:07.093080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
553600 293
 
1.5%
830400 259
 
1.4%
622800 242
 
1.3%
692000 237
 
1.2%
761200 236
 
1.2%
811900 211
 
1.1%
730710 184
 
1.0%
893090 181
 
0.9%
974280 177
 
0.9%
649520 175
 
0.9%
Other values (2347) 16901
88.5%
ValueCountFrequency (%)
3000 1
 
< 0.1%
3300 3
< 0.1%
3600 1
 
< 0.1%
11700 1
 
< 0.1%
13500 1
 
< 0.1%
13600 2
 
< 0.1%
15600 1
 
< 0.1%
17000 1
 
< 0.1%
19800 1
 
< 0.1%
24300 5
< 0.1%
ValueCountFrequency (%)
9010800 1
< 0.1%
9009600 1
< 0.1%
8459000 1
< 0.1%
8259900 1
< 0.1%
7613100 1
< 0.1%
7196400 1
< 0.1%
6988800 1
< 0.1%
6947100 1
< 0.1%
6767200 1
< 0.1%
6758100 1
< 0.1%

baggage
Real number (ℝ)

Zeros 

Distinct63
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20216.395
Minimum-90000
Maximum831600
Zeros17290
Zeros (%)90.5%
Negative2
Negative (%)< 0.1%
Memory size149.3 KiB
2025-04-27T15:57:07.213341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-90000
5-th percentile0
Q10
median0
Q30
95-th percentile198000
Maximum831600
Range921600
Interquartile range (IQR)0

Descriptive statistics

Standard deviation66560.501
Coefficient of variation (CV)3.2924021
Kurtosis15.648474
Mean20216.395
Median Absolute Deviation (MAD)0
Skewness3.6432106
Sum3.8605227 × 108
Variance4.4303002 × 109
MonotonicityNot monotonic
2025-04-27T15:57:07.335142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17290
90.5%
198000 275
 
1.4%
237600 268
 
1.4%
158400 263
 
1.4%
217800 260
 
1.4%
178200 256
 
1.3%
297000 48
 
0.3%
356400 43
 
0.2%
326700 23
 
0.1%
220000 23
 
0.1%
Other values (53) 347
 
1.8%
ValueCountFrequency (%)
-90000 2
 
< 0.1%
0 17290
90.5%
69300 1
 
< 0.1%
77000 1
 
< 0.1%
88000 17
 
0.1%
92400 3
 
< 0.1%
99000 10
 
0.1%
105600 9
 
< 0.1%
108000 1
 
< 0.1%
110000 13
 
0.1%
ValueCountFrequency (%)
831600 1
 
< 0.1%
762300 1
 
< 0.1%
712800 1
 
< 0.1%
693000 3
< 0.1%
653400 2
< 0.1%
620400 1
 
< 0.1%
594000 1
 
< 0.1%
554400 1
 
< 0.1%
544500 1
 
< 0.1%
534600 1
 
< 0.1%

insurance_fee
Real number (ℝ)

Skewed  Zeros 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.973508
Minimum-60000
Maximum60000
Zeros19053
Zeros (%)99.8%
Negative3
Negative (%)< 0.1%
Memory size149.3 KiB
2025-04-27T15:57:07.425329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-60000
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum60000
Range120000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2289.7353
Coefficient of variation (CV)23.13483
Kurtosis494.25637
Mean98.973508
Median Absolute Deviation (MAD)0
Skewness20.200008
Sum1889998.1
Variance5242887.7
MonotonicityNot monotonic
2025-04-27T15:57:07.503742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 19053
99.8%
50000 14
 
0.1%
40000 9
 
< 0.1%
45000 7
 
< 0.1%
60000 5
 
< 0.1%
55000 5
 
< 0.1%
-60000 1
 
< 0.1%
-1.1 1
 
< 0.1%
-0.8 1
 
< 0.1%
ValueCountFrequency (%)
-60000 1
 
< 0.1%
-1.1 1
 
< 0.1%
-0.8 1
 
< 0.1%
0 19053
99.8%
40000 9
 
< 0.1%
45000 7
 
< 0.1%
50000 14
 
0.1%
55000 5
 
< 0.1%
60000 5
 
< 0.1%
ValueCountFrequency (%)
60000 5
 
< 0.1%
55000 5
 
< 0.1%
50000 14
 
0.1%
45000 7
 
< 0.1%
40000 9
 
< 0.1%
0 19053
99.8%
-0.8 1
 
< 0.1%
-1.1 1
 
< 0.1%
-60000 1
 
< 0.1%

discount_amount
Real number (ℝ)

High correlation  Zeros 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24360.861
Minimum-24000
Maximum60000
Zeros6561
Zeros (%)34.4%
Negative2
Negative (%)< 0.1%
Memory size149.3 KiB
2025-04-27T15:57:07.586107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-24000
5-th percentile0
Q10
median20000
Q345000
95-th percentile60000
Maximum60000
Range84000
Interquartile range (IQR)45000

Descriptive statistics

Standard deviation21824.077
Coefficient of variation (CV)0.89586643
Kurtosis-1.4166822
Mean24360.861
Median Absolute Deviation (MAD)20000
Skewness0.26141733
Sum4.65195 × 108
Variance4.7629035 × 108
MonotonicityNot monotonic
2025-04-27T15:57:07.670471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 6561
34.4%
40000 1467
 
7.7%
60000 1455
 
7.6%
50000 1427
 
7.5%
45000 1425
 
7.5%
55000 1386
 
7.3%
22000 1126
 
5.9%
18000 1118
 
5.9%
24000 1059
 
5.5%
16000 1040
 
5.4%
Other values (3) 1032
 
5.4%
ValueCountFrequency (%)
-24000 1
 
< 0.1%
-18000 1
 
< 0.1%
0 6561
34.4%
16000 1040
 
5.4%
18000 1118
 
5.9%
20000 1030
 
5.4%
22000 1126
 
5.9%
24000 1059
 
5.5%
40000 1467
 
7.7%
45000 1425
 
7.5%
ValueCountFrequency (%)
60000 1455
7.6%
55000 1386
7.3%
50000 1427
7.5%
45000 1425
7.5%
40000 1467
7.7%
24000 1059
5.5%
22000 1126
5.9%
20000 1030
5.4%
18000 1118
5.9%
16000 1040
5.4%

Interactions

2025-04-27T15:57:00.995018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:56.733255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:57.556070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:58.213462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:58.879241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:59.533880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:00.291682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:01.097686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:56.850518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:57.660469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:58.315122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:58.975329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:59.632420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:00.396258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:01.188471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:56.948579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:57.736681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:58.404759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:59.071537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:59.715742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:00.491207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:01.295171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:57.053656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:57.833873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:58.500928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:59.168549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:59.813013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:00.595299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:01.383906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:57.153343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:57.930540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:58.590834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:59.257171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:00.010136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:00.690529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:01.475272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:57.252122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:58.032890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:58.685107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:59.345822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:00.106182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:00.795306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:01.571321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:57.450641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:58.127006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:58.782791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:56:59.441358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:00.202260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T15:57:00.896306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-27T15:57:07.751771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
airlinebaggagediscount_amountinsurance_feeorder_idpassenger_agepassenger_genderticket_idticket_priceticket_source_nameticket_status
airline1.0000.0690.2930.0090.0920.0000.0390.0940.1310.0340.070
baggage0.0691.0000.0260.0020.010-0.0040.0350.0100.0520.0070.067
discount_amount0.2930.0261.000-0.018-0.5030.0180.024-0.503-0.1770.0200.055
insurance_fee0.0090.002-0.0181.000-0.0070.0060.000-0.0070.0050.0000.000
order_id0.0920.010-0.503-0.0071.000-0.0800.0391.000-0.0760.0750.085
passenger_age0.000-0.0040.0180.006-0.0801.0000.000-0.0800.1330.0070.000
passenger_gender0.0390.0350.0240.0000.0390.0001.0000.0360.0170.0570.013
ticket_id0.0940.010-0.503-0.0071.000-0.0800.0361.000-0.0760.0720.084
ticket_price0.1310.052-0.1770.005-0.0760.1330.017-0.0761.0000.0340.050
ticket_source_name0.0340.0070.0200.0000.0750.0070.0570.0720.0341.0000.104
ticket_status0.0700.0670.0550.0000.0850.0000.0130.0840.0500.1041.000

Missing values

2025-04-27T15:57:01.750417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-27T15:57:01.962097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-27T15:57:02.315431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

order_idticket_idpassenger_idpassenger_genderpassenger_ageticket_source_nameticket_statuscreated_datedeparture_datedeparture_timearrival_datearrival_timefrom_to_locationfrom_toairlineseat_classticket_pricebaggageinsurance_feediscount_amount
02020531268Xs1fLinPQun+Oy/mD0FlfXMyJD38YlXJfXNbz+qO+5U=Female40.0IOSPaid2023-03-012023-03-1417:55:002023-03-1418:55:00BMV - SGNĐắk Lắk - Hồ Chí MinhRy/OjEDP12vfMWbnat5Mudc3PE4qIK/wwfCkBE5zGlM=I1_ECO86776000.045000
12019031242m/auVlEoaGSe2MqR/Lq4CFXJnI8zauNyF/KiFmMgXHs=Female40.0GYLPaid2023-03-012023-03-0209:30:002023-03-0210:30:00BMV - SGNĐắk Lắk - Hồ Chí MinhRy/OjEDP12vfMWbnat5Mudc3PE4qIK/wwfCkBE5zGlM=Z1_ECO97428000.045000
220245313376HvtV2vTqUfNBJ53C4TzvJnb0s74KgcRNuUVS+8jRvU=Female48.0IOSPaid2023-03-012023-04-1013:10:002023-04-1014:35:00HUI - SGNThừa Thiên Huế - Hồ Chí MinhRy/OjEDP12vfMWbnat5Mudc3PE4qIK/wwfCkBE5zGlM=J1_ECO142308000.016000
32025931360WiK2P31WEd4mckRNVWz0C2aKKRgSrauAG8NdfTLnKjE=Male29.0AndroidNew2023-03-012023-04-1013:10:002023-04-1014:35:00HUI - SGNThừa Thiên Huế - Hồ Chí MinhRy/OjEDP12vfMWbnat5Mudc3PE4qIK/wwfCkBE5zGlM=J1_ECO106731000.045000
420255313556HvtV2vTqUfNBJ53C4TzvJnb0s74KgcRNuUVS+8jRvU=Female42.0IOSPaid2023-03-012023-04-1013:10:002023-04-1014:35:00HUI - SGNThừa Thiên Huế - Hồ Chí MinhRy/OjEDP12vfMWbnat5Mudc3PE4qIK/wwfCkBE5zGlM=J1_ECO106731000.024000
520251313486HvtV2vTqUfNBJ53C4TzvJnb0s74KgcRNuUVS+8jRvU=Female42.0IOSPaid2023-03-012023-04-1013:10:002023-04-1014:35:00HUI - SGNThừa Thiên Huế - Hồ Chí MinhRy/OjEDP12vfMWbnat5Mudc3PE4qIK/wwfCkBE5zGlM=J1_ECO130449000.016000
62022631307WiK2P31WEd4mckRNVWz0C2aKKRgSrauAG8NdfTLnKjE=Male29.0AndroidNew2023-03-012023-04-1013:10:002023-04-1014:35:00HUI - SGNThừa Thiên Huế - Hồ Chí MinhRy/OjEDP12vfMWbnat5Mudc3PE4qIK/wwfCkBE5zGlM=J1_ECO130449000.00
720251313446HvtV2vTqUfNBJ53C4TzvJnb0s74KgcRNuUVS+8jRvU=Female49.0IOSPaid2023-03-012023-04-1013:10:002023-04-1014:35:00HUI - SGNThừa Thiên Huế - Hồ Chí MinhRy/OjEDP12vfMWbnat5Mudc3PE4qIK/wwfCkBE5zGlM=J1_ECO948720040000.018000
820251313466HvtV2vTqUfNBJ53C4TzvJnb0s74KgcRNuUVS+8jRvU=Female22.0IOSPaid2023-03-012023-04-1013:10:002023-04-1014:35:00HUI - SGNThừa Thiên Huế - Hồ Chí MinhRy/OjEDP12vfMWbnat5Mudc3PE4qIK/wwfCkBE5zGlM=J1_ECO94872000.024000
920251313506HvtV2vTqUfNBJ53C4TzvJnb0s74KgcRNuUVS+8jRvU=Male7.0IOSPaid2023-03-012023-04-1013:10:002023-04-1014:35:00HUI - SGNThừa Thiên Huế - Hồ Chí MinhRy/OjEDP12vfMWbnat5Mudc3PE4qIK/wwfCkBE5zGlM=J1_ECO101331000.024000
order_idticket_idpassenger_idpassenger_genderpassenger_ageticket_source_nameticket_statuscreated_datedeparture_datedeparture_timearrival_datearrival_timefrom_to_locationfrom_toairlineseat_classticket_pricebaggageinsurance_feediscount_amount
19086339095271061HLhjosTIRPTMqSCq6/bE3+3Bhj6tiXwRrXBqeNoAM=Female28.0IOSNew2023-05-312023-06-1117:40:002023-06-1119:30:00VDH - SGNQuảng Bình - Hồ Chí MinhV2Ej16fVems7wWmAIxP6MCYYH+i4YSHlvuJgKvD2q8A=Q213720000.00
190873391052714sAAP06aa+4i71WPrsr3K7AiVtBIZHkX/ZEwVVFIzc+E=Female28.0IOSPaid2023-05-312023-06-1117:40:002023-06-1119:30:00VDH - SGNQuảng Bình - Hồ Chí MinhV2Ej16fVems7wWmAIxP6MCYYH+i4YSHlvuJgKvD2q8A=Q160290000.00
190883391952727sMLGVyQ7cfMOpCM6c63F8aCfz9Xdn/Rc0E0VqRZEWwQ=Male28.0IOSNew2023-05-312023-06-1212:35:002023-06-1213:45:00DLI - SGNLâm Đồng - Hồ Chí MinhV2Ej16fVems7wWmAIxP6MCYYH+i4YSHlvuJgKvD2q8A=R143660000.00
1908933856526327MHTyxXVgw06aWI7UyjJojbXcNSAZ7IVQKKPA3OC4ZM=Female29.0GYLCancel2023-05-312023-06-1507:55:002023-06-1509:35:00DLI - SGNLâm Đồng - Hồ Chí MinhV2Ej16fVems7wWmAIxP6MCYYH+i4YSHlvuJgKvD2q8A=A6680002376000.00
1909033900526978NMW+DtI8tKi5qcf2nZvqHJUhvC5Ic4063AvCJXGIys=Female54.0GYLPaid2023-05-312023-06-0207:55:002023-06-0209:35:00DLI - SGNLâm Đồng - Hồ Chí MinhV2Ej16fVems7wWmAIxP6MCYYH+i4YSHlvuJgKvD2q8A=A66060000.00
1909133900526968NMW+DtI8tKi5qcf2nZvqHJUhvC5Ic4063AvCJXGIys=Male58.0GYLPaid2023-05-312023-06-0207:55:002023-06-0209:35:00DLI - SGNLâm Đồng - Hồ Chí MinhV2Ej16fVems7wWmAIxP6MCYYH+i4YSHlvuJgKvD2q8A=A80740000.00
1909233907527057MHTyxXVgw06aWI7UyjJojbXcNSAZ7IVQKKPA3OC4ZM=Female29.0AndroidCancel2023-05-312023-06-1007:55:002023-06-1009:35:00DLI - SGNLâm Đồng - Hồ Chí MinhV2Ej16fVems7wWmAIxP6MCYYH+i4YSHlvuJgKvD2q8A=E9100001584000.00
1909333854526307MHTyxXVgw06aWI7UyjJojbXcNSAZ7IVQKKPA3OC4ZM=Female29.0GYLNew2023-05-312023-06-1507:55:002023-06-1509:35:00DLI - SGNLâm Đồng - Hồ Chí MinhV2Ej16fVems7wWmAIxP6MCYYH+i4YSHlvuJgKvD2q8A=A53440000.00
190943388252668qUmeTJy0TEYBaZ6h6A+DRzmuD2SfdhcGxwsYUDGRpc8=Female25.0AndroidCancel2023-05-312023-06-0215:30:002023-06-0216:25:00DLI - SGNLâm Đồng - Hồ Chí MinhV2Ej16fVems7wWmAIxP6MCYYH+i4YSHlvuJgKvD2q8A=T110990000.00
190953388252669qUmeTJy0TEYBaZ6h6A+DRzmuD2SfdhcGxwsYUDGRpc8=Female50.0AndroidCancel2023-05-312023-06-0215:30:002023-06-0216:25:00DLI - SGNLâm Đồng - Hồ Chí MinhV2Ej16fVems7wWmAIxP6MCYYH+i4YSHlvuJgKvD2q8A=T110990000.00